Job Description
Are you ready to architect the intelligence of tomorrow?
Nexus Future Tech is seeking a visionary Senior AI/ML Architect to lead our next-generation generative AI initiatives. In this pivotal role, you will define the technical strategy for our large language models and drive innovation in automated reasoning systems.
We are looking for a builder who thrives in fast-paced environments and is passionate about pushing the boundaries of what's possible in artificial intelligence. You will be responsible for designing robust architectures that ensure scalability, security, and ethical AI practices.
Nexus Future Tech is seeking a visionary Senior AI/ML Architect to lead our next-generation generative AI initiatives. In this pivotal role, you will define the technical strategy for our large language models and drive innovation in automated reasoning systems.
We are looking for a builder who thrives in fast-paced environments and is passionate about pushing the boundaries of what's possible in artificial intelligence. You will be responsible for designing robust architectures that ensure scalability, security, and ethical AI practices.
Responsibilities
- Architect and deploy scalable machine learning infrastructure for large-scale NLP models.
- Lead research and development initiatives focused on Generative AI and Reinforcement Learning.
- Collaborate with cross-functional teams to integrate AI solutions into core products.
- Optimize model performance and reduce inference latency for real-time applications.
- Establish best practices for data governance, ethics in AI, and model monitoring.
- Present technical roadmaps and architectural decisions to executive stakeholders.
Qualifications
- Master’s or PhD in Computer Science, Mathematics, or a related technical field.
- 7+ years of professional experience in Machine Learning, Deep Learning, or Artificial Intelligence.
- Strong proficiency in Python, PyTorch, TensorFlow, or JAX.
- Proven track record of deploying production-ready ML models at scale.
- Experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker/Kubernetes).
- Familiarity with MLOps tools and model versioning (MLflow, Kubeflow).